Hi All,
When I tried to do logistic regression (with high maximum number of iterations) I got the following warning message
Warning message: fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y,
As I checked from the Archive R-Help mails, it seems that this happens when
the dataset exhibits complete separation.
Yes. correct.
However, p-values tend to 1
The reported p-values cannot be trusted: the asymptotic theory on which they are based is not valid in such circumstances.
, and residual deviance tends to 0.
Yes, this happens under complete separation: the model fits the observed 0/1 data perfectly.
My questions then is: -Is the converged model correct?
Well, "converged" is not really the right word to use -- the iterative algorithm has diverged. At least one of the coefficients has its MLE at infinity (or minus infinity). In that sense what you see reported (ie large values of estimated log odds-ratios, which approximate infinity) is correct. Still more correct would be estimates reported as Inf or -Inf: but the algorithm is not programmed to detect such divergence.
or -Can I limit the number of iterations in order to avoid this warning?
Yes, probably, but this is not a sensible course of action. The iterations are iterations of an algorithm to compute the MLE. The MLE is not finite-valued, and the warning is a clue to that.
If you *really* want finite parameter estimates, the answer is not to use maximum likelihood as the method of estimation. Various alternatives exist, mostly based on penalizing the likelihood [one such is in the brlr package, but there are others]. As a general principle surely it's better to maximize a different criterion (eg a penalized likelihood, with a purposefully chosen penalty function) rather than stop the MLE algorithm prematurely and arbitrarily?
I hope this helps!
David
Professor David Firth Dept of Statistics University of Warwick Coventry CV4 7AL United Kingdom
Email: [EMAIL PROTECTED] Voice: +44 (0)247 657 2581 Fax: +44 (0)247 652 4532
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